Abstract
In a world filled with data, it is expected for a nation to take decisions informed by data. However, countries need to first collect and publish such data in a way meaningful for both citizens and policy makers. A good thematic classification could be instrumental in helping users to navigate and find the right resources on a rich data repository, such as the one collected by the DANE (Departamento Administrativo Nacional de Estadística, i.e. the Colombia’s National Administrative Department of Statistics). The Visual Analytics Framework is a methodology for conducting visual analysis developed by T. Munzner et al.1 that could help with this task. This paper presents a case study applying such framework conducted to help the DANE to better visualize their data repository, and also to understand it better by using another classification extracted from its metadata. It describes the three main analysis tasks identified and the proposed solutions. Usability testing results during the process helped to correct the visualizations and make them adapted to decision-making. Finally, we explained the collection of insights generated from them.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
OECD: OECD Digital Government Studies Digital Government Review of Colombia Towards a Citizen-Driven Public Sector. OECD Publishing, France (2018). https://doi.org/10.1787/9789264291867-en
Munzner, T.: Visualization Analysis and Design, 1st edn. A K Peters Visualization Series/CRC Press, Natick, Massachusetts (2014). ISBN 10: 9781466508910, ISBN 13: 978-1466508910
Elzer, S., Carberry, S., Zukerman, I.: The automated understanding of simple bar charts. Artif. Intell. 175(2), 526–555 (2011). https://doi.org/10.1016/j.artint.2010.10.003
Indratmo, Howorko, L., Boedianto, J., Daniel, B.: The efficacy of stacked bar charts in supporting single-attribute and overall-attribute comparisons. Vis. Inform. 2(3), 155–165 (2018). https://doi.org/10.1016/j.visinf.2018.09.002
Ochs, C., Geller, J., Perl, Y., Musen, M.A.: A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies. J. Biomed. Inform. 62, 90–105 (2016). https://doi.org/10.1016/j.jbi.2016.06.008
Shneiderman, B.: Tree visualization with tree-maps: 2-D space-filling approach. ACM Trans. Graph. 11(1), 92–99 (1992). https://doi.org/10.1145/102377.115768
Hilbert, M., Oh, P., Monge, P.: Evolution of what? A network approach for the detection of evolutionary forces. Soc. Netw. 47, 38–46 (2016). https://doi.org/10.1016/j.socnet.2016.04.003
Petrini, M.A., Rocha, J., Brown, J.C., Bispo, R.: Using an analytic hierarchy process approach to prioritize public policies addressing family farming in Brazil. Land Use Policy 51, 85–94 (2016). https://doi.org/10.1016/j.landusepol.2015.10.029
Liu, S., et al.: Steering data quality with visual analytics: the complexity challenge. Vis. Inform. 2(4), 191–197 (2018). https://doi.org/10.1016/j.visinf.2018.12.001
Kamdar, M.R., Walk, S., Tudorache, T., Musen, M.A.: Analyzing user interactions with biomedical ontologies: a visual perspective. J. Web Semant. 49, 16–30 (2018). https://doi.org/10.1016/j.websem.2017.12.002
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Raimbaud, P., Espitia Castillo, J.C., Guerra-Gomez, J.A. (2019). A Visual Analytics Framework Case Study: Understanding Colombia’s National Administrative Department of Statistics Datasets. In: Ruiz, P., Agredo-Delgado, V. (eds) Human-Computer Interaction. HCI-COLLAB 2019. Communications in Computer and Information Science, vol 1114. Springer, Cham. https://doi.org/10.1007/978-3-030-37386-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-37386-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37385-6
Online ISBN: 978-3-030-37386-3
eBook Packages: Computer ScienceComputer Science (R0)